Overview

Dataset statistics

Number of variables27
Number of observations330
Missing cells636
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.7 KiB
Average record size in memory216.4 B

Variable types

Categorical5
Numeric22

Alerts

Name has a high cardinality: 329 distinct values High cardinality
summit_elev is highly correlated with vertical_drop and 7 other fieldsHigh correlation
vertical_drop is highly correlated with summit_elev and 11 other fieldsHigh correlation
base_elev is highly correlated with summit_elev and 4 other fieldsHigh correlation
fastQuads is highly correlated with vertical_drop and 9 other fieldsHigh correlation
triple is highly correlated with total_chairsHigh correlation
surface is highly correlated with total_chairsHigh correlation
total_chairs is highly correlated with fastQuads and 6 other fieldsHigh correlation
Runs is highly correlated with summit_elev and 13 other fieldsHigh correlation
TerrainParks is highly correlated with total_chairsHigh correlation
LongestRun_mi is highly correlated with summit_elev and 8 other fieldsHigh correlation
SkiableTerrain_ac is highly correlated with summit_elev and 12 other fieldsHigh correlation
Snow Making_ac is highly correlated with vertical_drop and 10 other fieldsHigh correlation
daysOpenLastYear is highly correlated with summit_elev and 9 other fieldsHigh correlation
averageSnowfall is highly correlated with summit_elev and 8 other fieldsHigh correlation
AdultWeekday is highly correlated with summit_elev and 11 other fieldsHigh correlation
AdultWeekend is highly correlated with vertical_drop and 10 other fieldsHigh correlation
projectedDaysOpen is highly correlated with vertical_drop and 8 other fieldsHigh correlation
NightSkiing_ac is highly correlated with Runs and 4 other fieldsHigh correlation
summit_elev is highly correlated with vertical_drop and 5 other fieldsHigh correlation
vertical_drop is highly correlated with summit_elev and 12 other fieldsHigh correlation
base_elev is highly correlated with summit_elev and 2 other fieldsHigh correlation
trams is highly correlated with vertical_drop and 8 other fieldsHigh correlation
fastSixes is highly correlated with trams and 2 other fieldsHigh correlation
fastQuads is highly correlated with vertical_drop and 7 other fieldsHigh correlation
triple is highly correlated with total_chairsHigh correlation
surface is highly correlated with total_chairsHigh correlation
total_chairs is highly correlated with vertical_drop and 11 other fieldsHigh correlation
Runs is highly correlated with summit_elev and 11 other fieldsHigh correlation
TerrainParks is highly correlated with total_chairsHigh correlation
LongestRun_mi is highly correlated with summit_elev and 8 other fieldsHigh correlation
SkiableTerrain_ac is highly correlated with vertical_drop and 2 other fieldsHigh correlation
Snow Making_ac is highly correlated with trams and 7 other fieldsHigh correlation
daysOpenLastYear is highly correlated with vertical_drop and 5 other fieldsHigh correlation
averageSnowfall is highly correlated with summit_elev and 6 other fieldsHigh correlation
AdultWeekday is highly correlated with summit_elev and 11 other fieldsHigh correlation
AdultWeekend is highly correlated with vertical_drop and 9 other fieldsHigh correlation
projectedDaysOpen is highly correlated with vertical_drop and 4 other fieldsHigh correlation
NightSkiing_ac is highly correlated with Snow Making_acHigh correlation
summit_elev is highly correlated with vertical_drop and 3 other fieldsHigh correlation
vertical_drop is highly correlated with summit_elev and 6 other fieldsHigh correlation
base_elev is highly correlated with summit_elevHigh correlation
fastQuads is highly correlated with vertical_drop and 3 other fieldsHigh correlation
total_chairs is highly correlated with TerrainParksHigh correlation
Runs is highly correlated with vertical_drop and 7 other fieldsHigh correlation
TerrainParks is highly correlated with total_chairsHigh correlation
LongestRun_mi is highly correlated with vertical_drop and 2 other fieldsHigh correlation
SkiableTerrain_ac is highly correlated with summit_elev and 7 other fieldsHigh correlation
Snow Making_ac is highly correlated with Runs and 2 other fieldsHigh correlation
daysOpenLastYear is highly correlated with projectedDaysOpenHigh correlation
averageSnowfall is highly correlated with summit_elev and 3 other fieldsHigh correlation
AdultWeekday is highly correlated with vertical_drop and 4 other fieldsHigh correlation
AdultWeekend is highly correlated with fastQuads and 2 other fieldsHigh correlation
projectedDaysOpen is highly correlated with daysOpenLastYearHigh correlation
NightSkiing_ac is highly correlated with SkiableTerrain_ac and 1 other fieldsHigh correlation
Region is highly correlated with stateHigh correlation
state is highly correlated with RegionHigh correlation
Region is highly correlated with state and 5 other fieldsHigh correlation
state is highly correlated with Region and 4 other fieldsHigh correlation
summit_elev is highly correlated with Region and 12 other fieldsHigh correlation
vertical_drop is highly correlated with Region and 18 other fieldsHigh correlation
base_elev is highly correlated with Region and 5 other fieldsHigh correlation
trams is highly correlated with vertical_drop and 10 other fieldsHigh correlation
fastEight is highly correlated with vertical_drop and 8 other fieldsHigh correlation
fastSixes is highly correlated with trams and 9 other fieldsHigh correlation
fastQuads is highly correlated with summit_elev and 16 other fieldsHigh correlation
quad is highly correlated with RunsHigh correlation
triple is highly correlated with fastEightHigh correlation
double is highly correlated with total_chairs and 1 other fieldsHigh correlation
surface is highly correlated with vertical_drop and 6 other fieldsHigh correlation
total_chairs is highly correlated with vertical_drop and 14 other fieldsHigh correlation
Runs is highly correlated with summit_elev and 14 other fieldsHigh correlation
TerrainParks is highly correlated with fastEight and 8 other fieldsHigh correlation
LongestRun_mi is highly correlated with summit_elev and 12 other fieldsHigh correlation
SkiableTerrain_ac is highly correlated with summit_elev and 14 other fieldsHigh correlation
Snow Making_ac is highly correlated with summit_elev and 12 other fieldsHigh correlation
daysOpenLastYear is highly correlated with Region and 6 other fieldsHigh correlation
yearsOpen is highly correlated with fastEightHigh correlation
averageSnowfall is highly correlated with Region and 12 other fieldsHigh correlation
AdultWeekday is highly correlated with summit_elev and 13 other fieldsHigh correlation
AdultWeekend is highly correlated with summit_elev and 13 other fieldsHigh correlation
projectedDaysOpen is highly correlated with vertical_drop and 3 other fieldsHigh correlation
NightSkiing_ac is highly correlated with summit_elev and 7 other fieldsHigh correlation
fastEight has 166 (50.3%) missing values Missing
Runs has 4 (1.2%) missing values Missing
TerrainParks has 51 (15.5%) missing values Missing
LongestRun_mi has 5 (1.5%) missing values Missing
Snow Making_ac has 46 (13.9%) missing values Missing
daysOpenLastYear has 51 (15.5%) missing values Missing
averageSnowfall has 14 (4.2%) missing values Missing
AdultWeekday has 54 (16.4%) missing values Missing
AdultWeekend has 51 (15.5%) missing values Missing
projectedDaysOpen has 47 (14.2%) missing values Missing
NightSkiing_ac has 143 (43.3%) missing values Missing
Name is uniformly distributed Uniform
fastSixes has 294 (89.1%) zeros Zeros
fastQuads has 222 (67.3%) zeros Zeros
quad has 171 (51.8%) zeros Zeros
triple has 106 (32.1%) zeros Zeros
double has 82 (24.8%) zeros Zeros
surface has 26 (7.9%) zeros Zeros

Reproduction

Analysis started2022-02-10 03:46:10.992507
Analysis finished2022-02-10 03:47:09.145509
Duration58.15 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct329
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Crystal Mountain
 
2
Mission Ridge
 
1
Sugarloaf
 
1
Nashoba Valley
 
1
Seven Springs
 
1
Other values (324)
324 

Length

Max length37
Median length16
Mean length16.38787879
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique328 ?
Unique (%)99.4%

Sample

1st rowAlyeska Resort
2nd rowEaglecrest Ski Area
3rd rowHilltop Ski Area
4th rowArizona Snowbowl
5th rowSunrise Park Resort

Common Values

ValueCountFrequency (%)
Crystal Mountain2
 
0.6%
Mission Ridge1
 
0.3%
Sugarloaf1
 
0.3%
Nashoba Valley1
 
0.3%
Seven Springs1
 
0.3%
Whaleback Mountain1
 
0.3%
Camden Snow Bowl1
 
0.3%
Timberline Four Seasons1
 
0.3%
Snow Creek1
 
0.3%
Yosemite Ski & Snowboard Area1
 
0.3%
Other values (319)319
96.7%

Length

2022-02-09T22:47:09.220309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mountain98
 
11.4%
ski88
 
10.2%
resort63
 
7.3%
area53
 
6.2%
valley20
 
2.3%
mt15
 
1.7%
peak11
 
1.3%
ridge11
 
1.3%
snow11
 
1.3%
10
 
1.2%
Other values (367)481
55.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
New York
33 
Michigan
29 
Sierra Nevada
22 
Colorado
22 
Pennsylvania
 
19
Other values (33)
205 

Length

Max length19
Median length8
Mean length9.03030303
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.2%

Sample

1st rowAlaska
2nd rowAlaska
3rd rowAlaska
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
New York33
 
10.0%
Michigan29
 
8.8%
Sierra Nevada22
 
6.7%
Colorado22
 
6.7%
Pennsylvania19
 
5.8%
Wisconsin16
 
4.8%
New Hampshire16
 
4.8%
Vermont15
 
4.5%
Minnesota14
 
4.2%
Idaho12
 
3.6%
Other values (28)132
40.0%

Length

2022-02-09T22:47:09.331013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new60
 
13.6%
york33
 
7.5%
michigan29
 
6.6%
nevada24
 
5.4%
sierra22
 
5.0%
colorado22
 
5.0%
pennsylvania19
 
4.3%
wisconsin16
 
3.6%
hampshire16
 
3.6%
vermont15
 
3.4%
Other values (36)186
42.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

state
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
New York
33 
Michigan
29 
Colorado
22 
California
21 
Pennsylvania
 
19
Other values (30)
206 

Length

Max length14
Median length8
Mean length8.572727273
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st rowAlaska
2nd rowAlaska
3rd rowAlaska
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
New York33
 
10.0%
Michigan29
 
8.8%
Colorado22
 
6.7%
California21
 
6.4%
Pennsylvania19
 
5.8%
New Hampshire16
 
4.8%
Wisconsin16
 
4.8%
Vermont15
 
4.5%
Minnesota14
 
4.2%
Utah13
 
3.9%
Other values (25)132
40.0%

Length

2022-02-09T22:47:09.428752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new60
 
14.9%
york33
 
8.2%
michigan29
 
7.2%
colorado22
 
5.5%
california21
 
5.2%
pennsylvania19
 
4.7%
hampshire16
 
4.0%
wisconsin16
 
4.0%
vermont15
 
3.7%
minnesota14
 
3.5%
Other values (29)158
39.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

summit_elev
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct266
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4591.818182
Minimum315
Maximum13487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:09.519509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum315
5-th percentile740.35
Q11403.75
median3127.5
Q37806
95-th percentile11500
Maximum13487
Range13172
Interquartile range (IQR)6402.25

Descriptive statistics

Standard deviation3735.535934
Coefficient of variation (CV)0.8135200015
Kurtosis-0.8956332107
Mean4591.818182
Median Absolute Deviation (MAD)2052.5
Skewness0.7066007985
Sum1515300
Variance13954228.71
MonotonicityNot monotonic
2022-02-09T22:47:09.633205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18006
 
1.8%
82005
 
1.5%
8505
 
1.5%
20005
 
1.5%
12004
 
1.2%
11004
 
1.2%
22504
 
1.2%
32003
 
0.9%
12503
 
0.9%
36003
 
0.9%
Other values (256)288
87.3%
ValueCountFrequency (%)
3151
 
0.3%
4201
 
0.3%
4402
0.6%
4501
 
0.3%
4951
 
0.3%
5003
0.9%
5251
 
0.3%
5281
 
0.3%
5401
 
0.3%
6351
 
0.3%
ValueCountFrequency (%)
134871
0.3%
131501
0.3%
130501
0.3%
130101
0.3%
129981
0.3%
125101
0.3%
124811
0.3%
124081
0.3%
123131
0.3%
121621
0.3%

vertical_drop
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct193
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1215.427273
Minimum60
Maximum4425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:09.749893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile230
Q1461.25
median964.5
Q31800
95-th percentile3115.4
Maximum4425
Range4365
Interquartile range (IQR)1338.75

Descriptive statistics

Standard deviation947.8645568
Coefficient of variation (CV)0.779861188
Kurtosis0.5618183039
Mean1215.427273
Median Absolute Deviation (MAD)599
Skewness1.082975179
Sum401091
Variance898447.2181
MonotonicityNot monotonic
2022-02-09T22:47:09.940384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70012
 
3.6%
50011
 
3.3%
30010
 
3.0%
100010
 
3.0%
6008
 
2.4%
3507
 
2.1%
15007
 
2.1%
4006
 
1.8%
12006
 
1.8%
16006
 
1.8%
Other values (183)247
74.8%
ValueCountFrequency (%)
601
 
0.3%
1001
 
0.3%
1301
 
0.3%
1751
 
0.3%
1801
 
0.3%
2003
0.9%
2102
0.6%
2111
 
0.3%
2141
 
0.3%
2201
 
0.3%
ValueCountFrequency (%)
44251
0.3%
44061
0.3%
43501
0.3%
41391
0.3%
36901
0.3%
36681
0.3%
35001
0.3%
34501
0.3%
34301
0.3%
34001
0.3%

base_elev
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct244
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3374
Minimum70
Maximum10800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:10.049093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile391.75
Q1869
median1561.5
Q36325.25
95-th percentile9200
Maximum10800
Range10730
Interquartile range (IQR)5456.25

Descriptive statistics

Standard deviation3117.121621
Coefficient of variation (CV)0.9238653292
Kurtosis-0.8659961402
Mean3374
Median Absolute Deviation (MAD)1047.5
Skewness0.7755201527
Sum1113420
Variance9716447.198
MonotonicityNot monotonic
2022-02-09T22:47:10.159797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60010
 
3.0%
8007
 
2.1%
10006
 
1.8%
12004
 
1.2%
12504
 
1.2%
92004
 
1.2%
13004
 
1.2%
4503
 
0.9%
16003
 
0.9%
65003
 
0.9%
Other values (234)282
85.5%
ValueCountFrequency (%)
701
0.3%
781
0.3%
1002
0.6%
1261
0.3%
1501
0.3%
1701
0.3%
2001
0.3%
2101
0.3%
2401
0.3%
2501
0.3%
ValueCountFrequency (%)
108001
 
0.3%
107901
 
0.3%
107801
 
0.3%
105001
 
0.3%
104001
 
0.3%
103501
 
0.3%
103001
 
0.3%
98201
 
0.3%
97121
 
0.3%
96003
0.9%

trams
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
293 
1
 
24
2
 
7
3
 
5
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0293
88.8%
124
 
7.3%
27
 
2.1%
35
 
1.5%
41
 
0.3%

Length

2022-02-09T22:47:10.264517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-09T22:47:10.325354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0293
88.8%
124
 
7.3%
27
 
2.1%
35
 
1.5%
41
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fastEight
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.2%
Missing166
Missing (%)50.3%
Memory size2.7 KiB
0.0
163 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0163
49.4%
1.01
 
0.3%
(Missing)166
50.3%

Length

2022-02-09T22:47:10.420101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-09T22:47:10.480938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0163
99.4%
1.01
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fastSixes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1848484848
Minimum0
Maximum6
Zeros294
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:10.533797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6516853121
Coefficient of variation (CV)3.525510705
Kurtosis32.75199621
Mean0.1848484848
Median Absolute Deviation (MAD)0
Skewness5.098452108
Sum61
Variance0.424693746
MonotonicityNot monotonic
2022-02-09T22:47:10.603610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0294
89.1%
121
 
6.4%
211
 
3.3%
31
 
0.3%
41
 
0.3%
51
 
0.3%
61
 
0.3%
ValueCountFrequency (%)
0294
89.1%
121
 
6.4%
211
 
3.3%
31
 
0.3%
41
 
0.3%
51
 
0.3%
61
 
0.3%
ValueCountFrequency (%)
61
 
0.3%
51
 
0.3%
41
 
0.3%
31
 
0.3%
211
 
3.3%
121
 
6.4%
0294
89.1%

fastQuads
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.018181818
Minimum0
Maximum15
Zeros222
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:10.687386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.198293694
Coefficient of variation (CV)2.159038449
Kurtosis14.63891238
Mean1.018181818
Median Absolute Deviation (MAD)0
Skewness3.443218577
Sum336
Variance4.832495164
MonotonicityNot monotonic
2022-02-09T22:47:10.763183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0222
67.3%
135
 
10.6%
229
 
8.8%
315
 
4.5%
48
 
2.4%
55
 
1.5%
65
 
1.5%
73
 
0.9%
92
 
0.6%
152
 
0.6%
Other values (4)4
 
1.2%
ValueCountFrequency (%)
0222
67.3%
135
 
10.6%
229
 
8.8%
315
 
4.5%
48
 
2.4%
55
 
1.5%
65
 
1.5%
73
 
0.9%
81
 
0.3%
92
 
0.6%
ValueCountFrequency (%)
152
 
0.6%
131
 
0.3%
111
 
0.3%
101
 
0.3%
92
 
0.6%
81
 
0.3%
73
 
0.9%
65
1.5%
55
1.5%
48
2.4%

quad
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9333333333
Minimum0
Maximum8
Zeros171
Zeros (%)51.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:10.847957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.312244572
Coefficient of variation (CV)1.405976327
Kurtosis5.243245037
Mean0.9333333333
Median Absolute Deviation (MAD)0
Skewness1.958798422
Sum308
Variance1.721985816
MonotonicityNot monotonic
2022-02-09T22:47:10.923754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0171
51.8%
178
23.6%
243
 
13.0%
322
 
6.7%
48
 
2.4%
56
 
1.8%
82
 
0.6%
ValueCountFrequency (%)
0171
51.8%
178
23.6%
243
 
13.0%
322
 
6.7%
48
 
2.4%
56
 
1.8%
82
 
0.6%
ValueCountFrequency (%)
82
 
0.6%
56
 
1.8%
48
 
2.4%
322
 
6.7%
243
 
13.0%
178
23.6%
0171
51.8%

triple
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum8
Zeros106
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:11.009525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.619129565
Coefficient of variation (CV)1.07941971
Kurtosis1.902804974
Mean1.5
Median Absolute Deviation (MAD)1
Skewness1.400540429
Sum495
Variance2.621580547
MonotonicityNot monotonic
2022-02-09T22:47:11.086320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0106
32.1%
199
30.0%
253
16.1%
337
 
11.2%
414
 
4.2%
59
 
2.7%
67
 
2.1%
74
 
1.2%
81
 
0.3%
ValueCountFrequency (%)
0106
32.1%
199
30.0%
253
16.1%
337
 
11.2%
414
 
4.2%
59
 
2.7%
67
 
2.1%
74
 
1.2%
81
 
0.3%
ValueCountFrequency (%)
81
 
0.3%
74
 
1.2%
67
 
2.1%
59
 
2.7%
414
 
4.2%
337
 
11.2%
253
16.1%
199
30.0%
0106
32.1%

double
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.833333333
Minimum0
Maximum14
Zeros82
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:11.169098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.81502789
Coefficient of variation (CV)0.9900152127
Kurtosis6.813289237
Mean1.833333333
Median Absolute Deviation (MAD)1
Skewness1.824020659
Sum605
Variance3.294326241
MonotonicityNot monotonic
2022-02-09T22:47:11.243898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
189
27.0%
082
24.8%
270
21.2%
334
 
10.3%
425
 
7.6%
521
 
6.4%
66
 
1.8%
91
 
0.3%
101
 
0.3%
141
 
0.3%
ValueCountFrequency (%)
082
24.8%
189
27.0%
270
21.2%
334
 
10.3%
425
 
7.6%
521
 
6.4%
66
 
1.8%
91
 
0.3%
101
 
0.3%
141
 
0.3%
ValueCountFrequency (%)
141
 
0.3%
101
 
0.3%
91
 
0.3%
66
 
1.8%
521
 
6.4%
425
 
7.6%
334
 
10.3%
270
21.2%
189
27.0%
082
24.8%

surface
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.621212121
Minimum0
Maximum15
Zeros26
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:11.333658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.059636344
Coefficient of variation (CV)0.785757218
Kurtosis5.445738744
Mean2.621212121
Median Absolute Deviation (MAD)1
Skewness1.811234756
Sum865
Variance4.24210187
MonotonicityNot monotonic
2022-02-09T22:47:11.408458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
298
29.7%
173
22.1%
359
17.9%
026
 
7.9%
424
 
7.3%
522
 
6.7%
610
 
3.0%
77
 
2.1%
95
 
1.5%
84
 
1.2%
Other values (2)2
 
0.6%
ValueCountFrequency (%)
026
 
7.9%
173
22.1%
298
29.7%
359
17.9%
424
 
7.3%
522
 
6.7%
610
 
3.0%
77
 
2.1%
84
 
1.2%
95
 
1.5%
ValueCountFrequency (%)
151
 
0.3%
121
 
0.3%
95
 
1.5%
84
 
1.2%
77
 
2.1%
610
 
3.0%
522
 
6.7%
424
 
7.3%
359
17.9%
298
29.7%

total_chairs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.266666667
Minimum0
Maximum41
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:11.503205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median7
Q310
95-th percentile20
Maximum41
Range41
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.798682728
Coefficient of variation (CV)0.7014535558
Kurtosis8.779215137
Mean8.266666667
Median Absolute Deviation (MAD)3
Skewness2.499456619
Sum2728
Variance33.62472138
MonotonicityNot monotonic
2022-02-09T22:47:11.593962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
548
14.5%
637
11.2%
434
10.3%
832
9.7%
729
8.8%
326
7.9%
1021
 
6.4%
1217
 
5.2%
916
 
4.8%
1114
 
4.2%
Other values (21)56
17.0%
ValueCountFrequency (%)
01
 
0.3%
12
 
0.6%
29
 
2.7%
326
7.9%
434
10.3%
548
14.5%
637
11.2%
729
8.8%
832
9.7%
916
 
4.8%
ValueCountFrequency (%)
411
0.3%
401
0.3%
361
0.3%
341
0.3%
311
0.3%
281
0.3%
271
0.3%
252
0.6%
242
0.6%
222
0.6%

Runs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct110
Distinct (%)33.7%
Missing4
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean48.21472393
Minimum3
Maximum341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:11.698682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q119
median33
Q360
95-th percentile133.75
Maximum341
Range338
Interquartile range (IQR)41

Descriptive statistics

Standard deviation46.36407659
Coefficient of variation (CV)0.9616165522
Kurtosis12.28031553
Mean48.21472393
Median Absolute Deviation (MAD)17.5
Skewness2.858197742
Sum15718
Variance2149.627598
MonotonicityNot monotonic
2022-02-09T22:47:11.800410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1412
 
3.6%
2311
 
3.3%
179
 
2.7%
279
 
2.7%
109
 
2.7%
168
 
2.4%
118
 
2.4%
157
 
2.1%
127
 
2.1%
457
 
2.1%
Other values (100)239
72.4%
ValueCountFrequency (%)
31
 
0.3%
61
 
0.3%
74
 
1.2%
82
 
0.6%
92
 
0.6%
109
2.7%
118
2.4%
127
2.1%
134
 
1.2%
1412
3.6%
ValueCountFrequency (%)
3411
0.3%
3361
0.3%
3171
0.3%
1951
0.3%
1871
0.3%
1701
0.3%
1691
0.3%
1671
0.3%
1661
0.3%
1621
0.3%

TerrainParks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)3.9%
Missing51
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean2.82078853
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:11.979930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.008112527
Coefficient of variation (CV)0.7118975796
Kurtosis4.337901964
Mean2.82078853
Median Absolute Deviation (MAD)1
Skewness1.754027868
Sum787
Variance4.032515923
MonotonicityNot monotonic
2022-02-09T22:47:12.065700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
181
24.5%
276
23.0%
346
13.9%
431
 
9.4%
517
 
5.2%
612
 
3.6%
76
 
1.8%
84
 
1.2%
93
 
0.9%
102
 
0.6%
(Missing)51
15.5%
ValueCountFrequency (%)
181
24.5%
276
23.0%
346
13.9%
431
 
9.4%
517
 
5.2%
612
 
3.6%
76
 
1.8%
84
 
1.2%
93
 
0.9%
102
 
0.6%
ValueCountFrequency (%)
141
 
0.3%
102
 
0.6%
93
 
0.9%
84
 
1.2%
76
 
1.8%
612
 
3.6%
517
 
5.2%
431
9.4%
346
13.9%
276
23.0%

LongestRun_mi
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)12.9%
Missing5
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1.433230769
Minimum0
Maximum6
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:12.164436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.5
median1
Q32
95-th percentile3.5
Maximum6
Range6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.156171187
Coefficient of variation (CV)0.8066887845
Kurtosis2.303044697
Mean1.433230769
Median Absolute Deviation (MAD)0.6
Skewness1.415104068
Sum465.8
Variance1.336731814
MonotonicityNot monotonic
2022-02-09T22:47:12.258186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
153
16.1%
1.525
 
7.6%
224
 
7.3%
319
 
5.8%
0.218
 
5.5%
0.318
 
5.5%
1.217
 
5.2%
0.417
 
5.2%
0.516
 
4.8%
2.516
 
4.8%
Other values (32)102
30.9%
ValueCountFrequency (%)
01
 
0.3%
0.112
3.6%
0.218
5.5%
0.318
5.5%
0.417
5.2%
0.516
4.8%
0.611
3.3%
0.76
 
1.8%
0.813
3.9%
0.93
 
0.9%
ValueCountFrequency (%)
63
0.9%
5.51
 
0.3%
5.31
 
0.3%
51
 
0.3%
4.91
 
0.3%
4.61
 
0.3%
4.52
 
0.6%
44
1.2%
3.71
 
0.3%
3.56
1.8%

SkiableTerrain_ac
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct193
Distinct (%)59.0%
Missing3
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean739.8012232
Minimum8
Maximum26819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:12.362906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile30
Q185
median200
Q3690
95-th percentile2814.2
Maximum26819
Range26811
Interquartile range (IQR)605

Descriptive statistics

Standard deviation1816.167441
Coefficient of variation (CV)2.454939764
Kurtosis132.0800602
Mean739.8012232
Median Absolute Deviation (MAD)155
Skewness9.829429557
Sum241915
Variance3298464.172
MonotonicityNot monotonic
2022-02-09T22:47:12.468623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10015
 
4.5%
2008
 
2.4%
508
 
2.4%
457
 
2.1%
406
 
1.8%
306
 
1.8%
606
 
1.8%
4005
 
1.5%
355
 
1.5%
705
 
1.5%
Other values (183)256
77.6%
ValueCountFrequency (%)
81
 
0.3%
91
 
0.3%
101
 
0.3%
121
 
0.3%
153
0.9%
161
 
0.3%
201
 
0.3%
231
 
0.3%
253
0.9%
261
 
0.3%
ValueCountFrequency (%)
268191
0.3%
84641
0.3%
73001
0.3%
58001
0.3%
55171
0.3%
52891
0.3%
48001
0.3%
43181
0.3%
35001
0.3%
31701
0.3%

Snow Making_ac
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct143
Distinct (%)50.4%
Missing46
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean174.8732394
Minimum2
Maximum3379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:12.574340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile18
Q150
median100
Q3200.5
95-th percentile600
Maximum3379
Range3377
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation261.3361254
Coefficient of variation (CV)1.494431774
Kurtosis81.64659565
Mean174.8732394
Median Absolute Deviation (MAD)65
Skewness7.365333703
Sum49664
Variance68296.57045
MonotonicityNot monotonic
2022-02-09T22:47:12.678063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10015
 
4.5%
3013
 
3.9%
509
 
2.7%
2008
 
2.4%
608
 
2.4%
1507
 
2.1%
357
 
2.1%
706
 
1.8%
456
 
1.8%
805
 
1.5%
Other values (133)200
60.6%
(Missing)46
 
13.9%
ValueCountFrequency (%)
22
0.6%
52
0.6%
81
 
0.3%
91
 
0.3%
102
0.6%
131
 
0.3%
151
 
0.3%
164
1.2%
182
0.6%
204
1.2%
ValueCountFrequency (%)
33791
0.3%
15001
0.3%
7501
0.3%
7001
0.3%
6801
0.3%
6621
0.3%
6601
0.3%
6581
0.3%
6541
0.3%
6501
0.3%

daysOpenLastYear
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct111
Distinct (%)39.8%
Missing51
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean115.1039427
Minimum3
Maximum305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:12.793754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile62.8
Q197
median114
Q3135
95-th percentile165
Maximum305
Range302
Interquartile range (IQR)38

Descriptive statistics

Standard deviation35.06325146
Coefficient of variation (CV)0.304622506
Kurtosis3.572011786
Mean115.1039427
Median Absolute Deviation (MAD)20
Skewness0.7075014465
Sum32114
Variance1229.431603
MonotonicityNot monotonic
2022-02-09T22:47:12.898474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10021
 
6.4%
12011
 
3.3%
11011
 
3.3%
1309
 
2.7%
1059
 
2.7%
1507
 
2.1%
1226
 
1.8%
756
 
1.8%
1215
 
1.5%
805
 
1.5%
Other values (101)189
57.3%
(Missing)51
 
15.5%
ValueCountFrequency (%)
31
0.3%
191
0.3%
321
0.3%
401
0.3%
421
0.3%
451
0.3%
471
0.3%
531
0.3%
561
0.3%
572
0.6%
ValueCountFrequency (%)
3051
0.3%
2431
0.3%
2301
0.3%
2051
0.3%
2001
0.3%
1921
0.3%
1881
0.3%
1851
0.3%
1841
0.3%
1821
0.3%

yearsOpen
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)21.9%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean63.65653495
Minimum6
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:13.014164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21.4
Q150
median58
Q369
95-th percentile83
Maximum2019
Range2013
Interquartile range (IQR)19

Descriptive statistics

Standard deviation109.4299279
Coefficient of variation (CV)1.719068245
Kurtosis313.5077878
Mean63.65653495
Median Absolute Deviation (MAD)9
Skewness17.49401884
Sum20943
Variance11974.90911
MonotonicityNot monotonic
2022-02-09T22:47:13.119881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5817
 
5.2%
5615
 
4.5%
5515
 
4.5%
5414
 
4.2%
5714
 
4.2%
8213
 
3.9%
6012
 
3.6%
8112
 
3.6%
8310
 
3.0%
5910
 
3.0%
Other values (62)197
59.7%
ValueCountFrequency (%)
61
 
0.3%
92
 
0.6%
121
 
0.3%
131
 
0.3%
151
 
0.3%
161
 
0.3%
171
 
0.3%
198
2.4%
211
 
0.3%
221
 
0.3%
ValueCountFrequency (%)
20191
 
0.3%
1041
 
0.3%
951
 
0.3%
871
 
0.3%
861
 
0.3%
851
 
0.3%
843
 
0.9%
8310
3.0%
8213
3.9%
8112
3.6%

averageSnowfall
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct107
Distinct (%)33.9%
Missing14
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean185.3164557
Minimum18
Maximum669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:13.234575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile30
Q169
median150
Q3300
95-th percentile437.25
Maximum669
Range651
Interquartile range (IQR)231

Descriptive statistics

Standard deviation136.3568422
Coefficient of variation (CV)0.7358053646
Kurtosis0.1855819989
Mean185.3164557
Median Absolute Deviation (MAD)100
Skewness0.8896517953
Sum58560
Variance18593.18843
MonotonicityNot monotonic
2022-02-09T22:47:13.338297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30022
 
6.7%
5017
 
5.2%
10015
 
4.5%
25014
 
4.2%
12012
 
3.6%
40011
 
3.3%
15010
 
3.0%
2009
 
2.7%
608
 
2.4%
3508
 
2.4%
Other values (97)190
57.6%
(Missing)14
 
4.2%
ValueCountFrequency (%)
181
 
0.3%
204
1.2%
245
1.5%
252
 
0.6%
261
 
0.3%
304
1.2%
312
 
0.6%
331
 
0.3%
341
 
0.3%
352
 
0.6%
ValueCountFrequency (%)
6691
 
0.3%
6631
 
0.3%
5501
 
0.3%
5451
 
0.3%
5007
2.1%
4861
 
0.3%
4621
 
0.3%
4602
 
0.6%
4591
 
0.3%
4303
0.9%

AdultWeekday
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)30.1%
Missing54
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean57.91695652
Minimum15
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:13.451993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile25.75
Q140
median50
Q371
95-th percentile106
Maximum179
Range164
Interquartile range (IQR)31

Descriptive statistics

Standard deviation26.14012554
Coefficient of variation (CV)0.4513380382
Kurtosis3.379800097
Mean57.91695652
Median Absolute Deviation (MAD)14
Skewness1.464065011
Sum15985.08
Variance683.3061631
MonotonicityNot monotonic
2022-02-09T22:47:13.550729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4916
 
4.8%
4512
 
3.6%
4010
 
3.0%
5010
 
3.0%
5510
 
3.0%
799
 
2.7%
308
 
2.4%
598
 
2.4%
427
 
2.1%
697
 
2.1%
Other values (73)179
54.2%
(Missing)54
 
16.4%
ValueCountFrequency (%)
151
 
0.3%
172
 
0.6%
205
1.5%
221
 
0.3%
231
 
0.3%
254
1.2%
261
 
0.3%
271
 
0.3%
282
 
0.6%
293
0.9%
ValueCountFrequency (%)
1791
0.3%
1691
0.3%
1581
0.3%
1491
0.3%
1391
0.3%
1351
0.3%
1252
0.6%
1192
0.6%
1161
0.3%
1151
0.3%

AdultWeekend
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct81
Distinct (%)29.0%
Missing51
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean64.16681004
Minimum17
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:13.749199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile34.9
Q147
median60
Q377.5
95-th percentile105.5
Maximum179
Range162
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation24.55458407
Coefficient of variation (CV)0.3826679876
Kurtosis3.584146903
Mean64.16681004
Median Absolute Deviation (MAD)15
Skewness1.414140983
Sum17902.54
Variance602.9275988
MonotonicityNot monotonic
2022-02-09T22:47:13.851924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4913
 
3.9%
4512
 
3.6%
5912
 
3.6%
6511
 
3.3%
4710
 
3.0%
609
 
2.7%
798
 
2.4%
507
 
2.1%
557
 
2.1%
897
 
2.1%
Other values (71)183
55.5%
(Missing)51
 
15.5%
ValueCountFrequency (%)
171
 
0.3%
203
0.9%
252
0.6%
302
0.6%
323
0.9%
331
 
0.3%
342
0.6%
354
1.2%
35.341
 
0.3%
372
0.6%
ValueCountFrequency (%)
1791
 
0.3%
1691
 
0.3%
1591
 
0.3%
1581
 
0.3%
1391
 
0.3%
1351
 
0.3%
1252
0.6%
1193
0.9%
1161
 
0.3%
1151
 
0.3%

projectedDaysOpen
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct90
Distinct (%)31.8%
Missing47
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean120.0530035
Minimum30
Maximum305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:13.966617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile70
Q1100
median120
Q3139.5
95-th percentile164.9
Maximum305
Range275
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation31.04596255
Coefficient of variation (CV)0.258602131
Kurtosis4.558917737
Mean120.0530035
Median Absolute Deviation (MAD)20
Skewness0.7022662259
Sum33975
Variance963.8517906
MonotonicityNot monotonic
2022-02-09T22:47:14.063359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10027
 
8.2%
12019
 
5.8%
15013
 
3.9%
13012
 
3.6%
9011
 
3.3%
13510
 
3.0%
1449
 
2.7%
1108
 
2.4%
1407
 
2.1%
1386
 
1.8%
Other values (80)161
48.8%
(Missing)47
 
14.2%
ValueCountFrequency (%)
301
 
0.3%
381
 
0.3%
401
 
0.3%
422
 
0.6%
561
 
0.3%
581
 
0.3%
602
 
0.6%
651
 
0.3%
706
1.8%
752
 
0.6%
ValueCountFrequency (%)
3051
 
0.3%
2331
 
0.3%
2001
 
0.3%
1931
 
0.3%
1851
 
0.3%
1841
 
0.3%
1811
 
0.3%
1803
0.9%
1701
 
0.3%
1691
 
0.3%

NightSkiing_ac
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct78
Distinct (%)41.7%
Missing143
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean100.3957219
Minimum2
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-02-09T22:47:14.172068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q140
median72
Q3114
95-th percentile296.4
Maximum650
Range648
Interquartile range (IQR)74

Descriptive statistics

Standard deviation105.1696202
Coefficient of variation (CV)1.047550814
Kurtosis10.16707602
Mean100.3957219
Median Absolute Deviation (MAD)35
Skewness2.916687658
Sum18774
Variance11060.64901
MonotonicityNot monotonic
2022-02-09T22:47:14.272799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10018
 
5.5%
359
 
2.7%
309
 
2.7%
458
 
2.4%
408
 
2.4%
807
 
2.1%
2007
 
2.1%
507
 
2.1%
155
 
1.5%
605
 
1.5%
Other values (68)104
31.5%
(Missing)143
43.3%
ValueCountFrequency (%)
21
 
0.3%
81
 
0.3%
91
 
0.3%
103
0.9%
121
 
0.3%
141
 
0.3%
155
1.5%
161
 
0.3%
171
 
0.3%
203
0.9%
ValueCountFrequency (%)
6501
0.3%
6001
0.3%
5501
0.3%
5411
0.3%
5001
0.3%
4501
0.3%
4001
0.3%
3171
0.3%
3002
0.6%
2881
0.3%

Interactions

2022-02-09T22:47:04.913826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:13.028064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:17.950900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:20.280180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:22.457358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:24.830012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:27.243558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:29.677051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:32.148442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:34.565978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:36.933647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:39.102846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:41.500434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:43.799287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:46.040295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:48.318203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:50.771642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:53.079471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:55.432180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:57.857694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:00.218382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:02.613975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:05.015553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:13.396080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:18.054622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:20.382904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:22.565069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:24.938722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:27.357255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:29.792742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:32.256154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:34.673690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:37.031385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:39.209560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:41.600168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:43.905005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:46.144017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:48.422923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:50.877360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:53.192170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:55.541887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:57.962414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:00.333074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:02.718695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:05.121270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:13.745146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:18.150873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:20.471667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:22.659816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:25.034466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:27.460977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:29.890481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:32.350902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:34.767439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:37.117156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:39.306302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:41.691922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:43.996759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:46.234775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:48.518667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:50.969115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:53.291903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:55.638628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:58.054168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:00.432808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:02.901207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:05.213025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:13.851861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:18.249609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:20.565416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:22.756557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:25.134199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:27.568690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:30.083963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:32.449637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:34.866175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:37.208911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:39.407032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:41.786669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:44.092504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:46.420278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:48.618401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:51.066853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:53.396623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:55.740355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:58.150910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:00.536530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:02.993959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:47:05.325724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:14.191952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:18.349342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:20.664152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-09T22:46:22.854295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-09T22:47:14.694670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-09T22:47:14.992873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-09T22:47:15.261156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-09T22:47:15.392804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-09T22:47:07.294459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-09T22:47:08.406486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-09T22:47:08.680752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-09T22:47:08.993915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuadsquadtripledoublesurfacetotal_chairsRunsTerrainParksLongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
0Alyeska ResortAlaskaAlaska3939250025010.0022002776.02.01.01610.0113.0150.060.0669.065.085.0150.0550.0
1Eaglecrest Ski AreaAlaskaAlaska26001540120000.0000040436.01.02.0640.060.045.044.0350.047.053.090.0NaN
2Hilltop Ski AreaAlaskaAlaska2090294179600.0000102313.01.01.030.030.0150.036.069.030.034.0152.030.0
3Arizona SnowbowlArizonaArizona115002300920000.0102212855.04.02.0777.0104.0122.081.0260.089.089.0122.0NaN
4Sunrise Park ResortArizonaArizona11100180092000NaN012310765.02.01.2800.080.0115.049.0250.074.078.0104.080.0
5Yosemite Ski & Snowboard AreaNorthern CaliforniaCalifornia7800600720000.0000131510.02.00.488.0NaN110.084.0300.047.047.0107.0NaN
6Bear MountainSierra NevadaCalifornia88051665714000.00212341227.014.01.5198.0198.0122.076.0100.0NaNNaN130.0NaN
7Bear ValleySierra NevadaCalifornia85001900660000.01102421067.02.01.21680.0100.0165.052.0359.0NaNNaN151.0NaN
8Boreal Mountain ResortSierra NevadaCalifornia7700500720000.0011312833.06.01.0380.0200.0150.054.0400.049.0NaN150.0200.0
9Dodge RidgeSierra NevadaCalifornia82001600660000.00012541267.05.02.0862.0NaNNaN69.0350.078.078.0140.0NaN

Last rows

NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuadsquadtripledoublesurfacetotal_chairsRunsTerrainParksLongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
320Whitecap MountainWisconsinWisconsin175040012950NaN001040543.01.01.0400.0300.0105.057.0200.060.060.0118.0NaN
321Wilmot MountainWisconsinWisconsin10302308000NaN0032231023.02.00.5135.0135.0125.081.070.056.066.0139.0135.0
322Grand Targhee ResortWyomingWyoming99202270785100.0022001595.01.02.72602.0NaN152.050.0500.090.090.0152.0NaN
323Hogadon BasinWyomingWyoming800064074000NaN000011228.01.00.692.032.0121.061.080.048.048.095.0NaN
324Jackson HoleWyomingWyoming104504139631130.004412115130.06.04.52500.0195.0130.054.0459.0NaNNaN133.0NaN
325Meadowlark Ski LodgeWyomingWyoming9500100085000NaN000111314.01.01.5300.0NaNNaN9.0NaNNaNNaNNaNNaN
326Sleeping Giant Ski ResortWyomingWyoming7428810661900.0000111348.01.01.0184.018.061.081.0310.042.042.077.0NaN
327Snow King ResortWyomingWyoming7808157162370NaN001110332.02.01.0400.0250.0121.080.0300.059.059.0123.0110.0
328Snowy Range Ski & Recreation AreaWyomingWyoming9663990879800.0000131533.02.00.775.030.0131.059.0250.049.049.0NaNNaN
329White Pine Ski AreaWyomingWyoming9500110084000NaN000200225.0NaN0.4370.0NaNNaN81.0150.0NaN49.0NaNNaN